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numpy array with grids and associated operations

Project Description

SPACEGRIDS README.

The Python 2.7 Spacegrids module, “”Numpy on grids”, provides useful classes for analyzing spatial data defined on grids. It is designed to ensure consistency between data and grid information via interacting objects of fields, grids, coordinates and axes. It provides very simple expressions for complex operations, including volume and distance related concepts such as integration and differentiation.

Works with most models and datasets, including: UVic (all versions), CSIRO Mk3L, FAMOUS, CCM3.6 (Atmosphere), Levitus Data

On Ubuntu/ Debian, install dependencies via package manager if pip install fails:

apt-get install python-{tk,numpy,matplotlib,scipy}

The following import statement provides all functionality:

from spacegrids import sg


Documentation: https://github.com/willo12/spacegrids/wiki

DATA ORGANISATION ON DISK

Before using the sg module, Netcdf data must be organised inside directories you need to create in the following way. Create a directory named PROJECTS inside your home directory ($HOME) or on a storage disk (see below). This will contain all the data for all the projects. Then create a subdirectory for each of your projects. Inside each project, create a small text file named projname containing only a name, say “glacial”, to identify your project. In this case, type: echo glacial > projname within the project dir. All subdirectories inside the project directory will now be regarded as potential experiment directories and will be searched for netcdf files when a sg project object P is created (see below). If a netcdf file is found in any of these subdirectories, an exper (experiment) object of the same name as the directory is created and added to the dictionary of experiments belonging to the project object P. Experiment directories can have multiple netcdf files, and can be located inside subdirectories of project directories.

The following python modules are required:

numpy matplotlib scipy

It is possible to use Scientific.IO (for Netcdf) instead of scipy for Netcdf IO by setting a flag.

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